Efficient Cryo-Electron Tomogram Simulation of Macromolecular Crowding with Application to SARS-CoV-2

Abstract

We propose an efficient method for simulating a cryo-Electron Tomography (cryo-ET) image of a target macromolecule with several neighbor macromolecules packed to achieve a realistic crowded cytoplasm content. The simulated results are subtomograms with corresponding noise-free 3D density maps and pre-specified labels (PDB ID, center locations, and orientations) to assist bioimage analysis. They can serve as benchmark datasets for testing developing cryo-ET analysis algorithms and as training datasets with readily available ground truth labels for learning neural network models. The COVID-19 pandemic has sparked a global health crisis that severely impacting lives worldwide. As an important application, we simulated the scene of SARS-CoV-2 interacting with the host cell. The simulated cryo-ET images clearly showed the binding domain of the virus and the host cell to facilitate the research of SARS-CoV-2' infection. We also trained two different classification models to demonstrate that our simulated cryo-ET data is able to assist the cryo-ET analysis task and to validate the performance between different methods.

Self-supervised learning for macromolecular structure classification based on cryo-electron tomograms

“…Several different methods exist for simulating cryo-ET data ( Pei et al, 2016 ; Liu et al, 2020a , b ). Here, we use the framework designed by Liu et al ( Liu et al, 2020b ).…”

“…Several different methods exist for simulating cryo-ET data ( Pei et al, 2016 ; Liu et al, 2020a , b ). Here, we use the framework designed by Liu et al ( Liu et al, 2020b ). They proposed an efficient gradient descent based method to generate 3D cryo-ET subtomogram images of a target macromolecule with a crowded environment having several random neighbouring macromolecules.…”

“…Furthermore, the performance of deep supervised classification models relies on the number of labelled cryo-ET subtomograms ( Frazier et al, 2017 ). One strategy to tackle the scarcity of labelled data is to generate simulated cryo-ET data on which supervised models will be trained - an approach used by several previous studies that simulated cryo-ET subtomogram data ( Pei et al, 2016 ; Liu et al, 2020a , b ). There is also a recent study using simulated data for supervised training followed by application to experimental data ( Moebel and Kervrann, 2022 ).…”

Deep Active Learning for Cryo-Electron Tomography Classification

“…Simulated Data. We follow [20] to prepare the simulated data. All the simulated datasets consist of 50 classes but with three different SNRs (i.e.…”

Correlation-Based Analysis of COVID-19 Virus Genome Versus Other Fatal Virus Genomes

“…Their experimentation analysis claimed that the cosine-similarity-based algorithm is as good as the BLASTP [11] algorithm and the CD-HIT algorithm. Further, it also noticed that the CD-HIT algorithm uses a short word filter [12], which is not accurate, to compute similarity between any two sequences. The KD tree-and ball treebased approaches [13] used for genome sequences as a part of approximation nearest neighbour-based techniques had some flaws, for a KD tree all the instances of a sequence that are less than the median are placed in the left partition, and all that are greater than or equal to the median are placed in the right partition.…”